JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

Learning embeddings of a heterogeneous behavior network for potential behavior prediction

Author(s): Yue-yang Wang, Wei-hao Jiang, Shi-liang Pu, Yue-ting Zhuang
Affiliation(s): College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more
Corresponding email(s): yueyangw@zju.edu.cn, jiangweihao5@hikvision.com, pushiliang@hikvision.com, yzhuang@zju.edu.cn
Key Words: Network embedding, Representation learning, Human behavior, Social networks, Heterogeneous information network, Attribute

Share this article to： More <<< Previous Paper \|Next Paper >>>

Yue-yang Wang, Wei-hao Jiang, Shi-liang Pu, Yue-ting Zhuang. Learning embeddings of a heterogeneous behavior network for potential behavior prediction[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.1800493

@article{title="Learning embeddings of a heterogeneous behavior network for potential behavior prediction",
author="Yue-yang Wang, Wei-hao Jiang, Shi-liang Pu, Yue-ting Zhuang",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.1800493"
}

%0 Journal Article
%T Learning embeddings of a heterogeneous behavior network for potential behavior prediction
%A Yue-yang Wang
%A Wei-hao Jiang
%A Shi-liang Pu
%A Yue-ting Zhuang
%J Frontiers of Information Technology & Electronic Engineering
%P 422-435
%@ 2095-9184
%D in press
%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/FITEE.1800493"

TY - JOUR
T1 - Learning embeddings of a heterogeneous behavior network for potential behavior prediction
A1 - Yue-yang Wang
A1 - Wei-hao Jiang
A1 - Shi-liang Pu
A1 - Yue-ting Zhuang
J0 - Frontiers of Information Technology & Electronic Engineering
SP - 422
EP - 435
%@ 2095-9184
Y1 - in press
PB - Zhejiang University Press & Springer
ER -
doi="https://doi.org/10.1631/FITEE.1800493"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Potential behavior prediction involves understanding the latent human behavior of specific groups, and can assist organizations in making strategic decisions. Progress in information technology has made it possible to acquire more and more data about human behavior. In this paper, we examine behavior data obtained in real-world scenarios as an information network composed of two types of objects (humans and actions) associated with various attributes and three types of relationships (human-human, human-action, and action-action), which we call the heterogeneous behavior network (HBN). To exploit the abundance and heterogeneity of the HBN, we propose a novel network embedding method, human-action-attribute-aware heterogeneous network embedding (a⁴HNE), which jointly considers structural proximity, attribute resemblance, and heterogeneity fusion. Experiments on two real-world datasets show that this approach outperforms other similar methods on various heterogeneous information network mining tasks for potential behavior prediction.

面向潜在行为预测的异构行为网络嵌入学习

王悦阳^1,2，姜伟浩³，蒲世亮³，庄越挺¹
¹浙江大学计算机科学与技术学院，中国杭州市，310027
²重庆大学大数据与软件学院，中国重庆市，401331
³海康威视研究院，中国杭州市，310051

摘要：潜在行为预测即理解特定群体潜在的人类行为，可辅助组织做出战略决策。信息技术的进步使获取人类行为的庞大数据成为可能。本文将真实场景中获取的人类行为数据构建成信息网络；该信息网络由2种对象（人和动作）和3种关系（人–人、人–动作和动作–动作）组成，称作异构行为网络（HBN）。为充分利用异构行为网络的丰富性和异构性，提出一种网络嵌入方法，称作人–行为–属性感知的异构网络嵌入（a⁴HNE）；该方法综合考虑网络结构邻近性、节点属性相似性和异构性融合。在两个真实数据集上的实验结果表明，该方法在各种异构信息网络挖掘任务中的潜在行为预测性能优于其他同类方法。

关键词组：网络嵌入；表示学习；人类行为；社交网络；异构信息网络；属性

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Backstrom L, Leskovec J, 2011. Supervised random walks: predicting and recommending links in social networks. Proc 4^th ACM Int Conf on Web Search and Data Mining, p.635-644.

[2]Bhagat S, Cormode G, Muthukrishnan S, 2011. Node classification in social networks. In: Aggarwal C (Ed.), Social Network Data Analytics. Springer, Boston, p.115-148.

[3]Chang SY, Han W, Tang JL, et al., 2015. Heterogeneous network embedding via deep architectures. Proc 21^st ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.119-128.

[4]Chen T, Sun YZ, 2017. Task-guided and path-augmented heterogeneous network embedding for author identification. Proc 10^th ACM Int Conf on Web Search and Data Mining, p.295-304.

[5]Chen YX, Wang CG, 2017. HINE: heterogeneous information network embedding. Int Conf on Database Systems for Advanced Applications, p.180-195.

[6]Ding CHQ, He XF, Zha HY, et al., 2001. A min-max cut algorithm for graph partitioning and data clustering. Proc IEEE Int Conf on Data Mining, p.107-114.

[7]Dong YX, Chawla N, Swami A, 2017. metapath2vec: scalable representation learning for heterogeneous networks. Proc 23^rd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.135-144.

[8]Glorot X, Bordes A, Bengio Y, 2011. Deep sparse rectifier neural networks. Proc 14^th Int Conf on Artificial Intelligence and Statistics, p.315-323.

[9]Grover A, Leskovec J, 2016. node2vec: scalable feature learning for networks. Proc 22^nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.855-864.

[10]Hanley JA, McNeil BJ, 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1):29-36.

[11]Harris DM, Harris S, 2010. Digital Design and Computer Architecture. Morgan Kaufmann, Amsterdam.

[12]Huang ZP, Mamoulis N, 2017. Heterogeneous information network embedding for meta path based proximity. https://arxiv.org/abs/1701.05291

[13]Koren Y, 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. Proc 14^th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.426-434.

[14]Le Q, Mikolov T, 2014. Distributed representations of sentences and documents. Proc 31^st Int Conf on Machine Learning, p.1188-1196.

[15]Lerman K, Intagorn S, Kang JH, et al., 2012. Using proximity to predict activity in social networks. Proc 21^st Int Conf on World Wide Web, p.555-556.

[16]Liben-Nowell D, Kleinberg J, 2007. The link-prediction problem for social networks. J Am Soc Inform Sci Technol, 58(7):1019-1031.

[17]Ma H, Zhou DY, Liu C, et al., 2011. Recommender systems with social regularization. Proc 4^th ACM Int Conf on Web Search and Data Mining, p.287-296.

[18]Mikolov T, Sutskever I, Chen K, et al., 2013a. Distributed representations of words and phrases and their compositionality. Proc 26^th Int Conf on Neural Information Processing Systems, p.3111-3119.

[19]Mikolov T, Chen K, Corrado G, et al., 2013b. Efficient estimation of word representations in vector space. https://arxiv.org/abs/1301.3781

[20]Ou MD, Cui P, Pei J, et al., 2016. Asymmetric transitivity preserving graph embedding. Proc 22^nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.1105-1114.

[21]Pan SR, Wu J, Zhu XQ, et al., 2016. Tri-party deep network representation. Proc 25^th Int Joint Conf on Artificial Intelligence, p.1895-1901.

[22]Perozzi B, Al-Rfou R, Skiena S, 2014. DeepWalk: online learning of social representations. Proc 20^th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.701-710.

[23]Ribeiro LFR, Saverese PHP, Figueiredo DR, 2017. struc2vec: learning node representations from structural identity. Proc 23^rd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.385-394.

[24]Sen P, Namata G, Bilgic M, et al., 2008. Collective classification in network data. AI Mag, 29(3):93-106.

[25]Shi C, Hu BB, Zhao WX, et al., 2019. Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng, 31(2):357-370.

[26]Stallings J, Vance E, Yang JS, et al., 2013. Determining scientific impact using a collaboration index. PNAS, 110(24):9680-9685.

[27]Sun XF, Guo J, Ding X, et al., 2016. A general framework for content-enhanced network representation learning. https://arxiv.org/abs/1610.02906

[28]Sun Y, Han J, Yan X, et al., 2011. PathSim: meta path-based top- k similarity search in heterogeneous information networks. Proc VLDB Endowm, 4(11):992-1003.

[29]Tang J, Zhang J, Yao LM, et al., 2008. ArnetMiner: extraction and mining of academic social networks. Proc 14^th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.990-998.

[30]Tang J, Qu M, Wang MZ, et al., 2015a. LINE: large-scale information network embedding. Proc 24^th Int Conf on World Wide Web, p.1067-1077.

[31]Tang J, Qu M, Mei QZ, 2015b. PTE: predictive text embedding through large-scale heterogeneous text networks. Proc 21^st ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.1165-1174.

[32]Tang L, Liu H, 2009. Relational learning via latent social dimensions. Proc 15^th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.817-826.

[33]Tu C, Liu H, Liu Z, et al., 2017a. CANE: context-aware network embedding for relation modeling. Proc 55^th Annual Meeting of the Association for Computational Linguistics, p.1722-1731.

[34]Tu C, Zhang Z, Liu Z, et al., 2017b. TransNet: translation-based network representation learning for social relation extraction. Proc 26^th Int Joint Conf on Artificial Intelligence, p.2864-2870.

[35]van der Maaten L, Hinton G, 2008. Visualizing data using t-SNE. J Mach Learn Res, 9:2579-2605.

[36]Vazquez A, Flammini A, Maritan A, et al., 2003. Global protein function prediction from protein-protein interaction networks. Nat Biotechnol, 21:697-700.

[37]Yang C, Liu Z, Zhao D, et al., 2015. Network representation learning with rich text information. Proc 24^th Int Conf on Artificial Intelligence, p.2111-2117.

[38]Yang C, Sun M, Liu Z, et al., 2017. Fast network embedding enhancement via high order proximity approximation. Proc 26^th Int Joint Conf on Artificial Intelligence, p.3894-3900.

[39]Yin HZ, Hu ZT, Zhou XF, et al., 2016. Discovering interpretable geo-social communities for user behavior prediction. IEEE 32^nd Int Conf on Data Engineering, p.942-953.

[40]Zhang CX, Swami A, Chawla NV, 2018. CARL: content-aware representation learning for heterogeneous networks. https://arxiv.org/abs/1805.04983

Open peer comments: Debate/Discuss/Question/Opinion

<1>

- Go to

面向潜在行为预测的异构行为网络嵌入学习

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference